






"""simple docstring"""





import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__lowerCamelCase							=  logging.get_logger(__name__)



class       __A		:

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Dict					)			->							Dict:
											__magic_name__:	List[Any]							=       question_encoder
											__magic_name__:	Tuple							=       generator
											__magic_name__:	List[str]							=       self.question_encoder

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Tuple					)			->							Any:
											if os.path.isfile(__snake_case					):
																	raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'					)
											os.makedirs(__snake_case     ,		exist_ok=__snake_case					)
											__magic_name__:	Union[str, Any]							=       os.path.join(__snake_case     ,		"""question_encoder_tokenizer"""					)
											__magic_name__:	str							=       os.path.join(__snake_case     ,		"""generator_tokenizer"""					)
											self.question_encoder.save_pretrained(__snake_case					)
											self.generator.save_pretrained(__snake_case					)

					@classmethod
					def   lowerCamelCase__   (			cls  :      Tuple     ,		__snake_case  :      Optional[Any]     ,		**__snake_case  :      List[Any]					)			->							str:
											# dynamically import AutoTokenizer
											from ..auto.tokenization_auto import AutoTokenizer

											__magic_name__:	Optional[Any]							=       kwargs.pop("""config"""     ,		__snake_case					)

											if config is None:
																	__magic_name__:	Union[str, Any]							=       RagConfig.from_pretrained(__snake_case					)

											__magic_name__:	Optional[int]							=       AutoTokenizer.from_pretrained(
											    __snake_case     ,		config=config.question_encoder     ,		subfolder="""question_encoder_tokenizer"""					)
											__magic_name__:	Union[str, Any]							=       AutoTokenizer.from_pretrained(
											    __snake_case     ,		config=config.generator     ,		subfolder="""generator_tokenizer"""					)
											return cls(question_encoder=__snake_case     ,		generator=__snake_case					)

					def __call__(			self  :      Dict     ,		*__snake_case  :      Tuple     ,		**__snake_case  :      Optional[Any]					)			->							Tuple:
											return self.current_tokenizer(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		*__snake_case  :      Tuple     ,		**__snake_case  :      Optional[Any]					)			->							str:
											return self.generator.batch_decode(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		*__snake_case  :      Any     ,		**__snake_case  :      Dict					)			->							Optional[Any]:
											return self.generator.decode(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Dict:
											__magic_name__:	str							=       self.question_encoder

					def   lowerCamelCase__   (			self  :      Any					)			->							List[str]:
											__magic_name__:	Union[str, Any]							=       self.generator






					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      List[str]     ,		__snake_case  :      Optional[List[str]] = None     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      str = "longest"     ,		__snake_case  :      str = None     ,		__snake_case  :      bool = True     ,		**__snake_case  :      Optional[Any]     ,		)			->							BatchEncoding:
											warnings.warn(
											    """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
											    """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
											    """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
											    """details"""     ,		__snake_case     ,		)
											if max_length is None:
																	__magic_name__:	List[str]							=       self.current_tokenizer.model_max_length
											__magic_name__:	Dict							=       self(
											    __snake_case     ,		add_special_tokens=__snake_case     ,		return_tensors=__snake_case     ,		max_length=__snake_case     ,		padding=__snake_case     ,		truncation=__snake_case     ,		**__snake_case     ,		)
											if tgt_texts is None:
																	return model_inputs
											# Process tgt_texts
											if max_target_length is None:
																	__magic_name__:	Dict							=       self.current_tokenizer.model_max_length
											__magic_name__:	Union[str, Any]							=       self(
											    text_target=__snake_case     ,		add_special_tokens=__snake_case     ,		return_tensors=__snake_case     ,		padding=__snake_case     ,		max_length=__snake_case     ,		truncation=__snake_case     ,		**__snake_case     ,		)
											__magic_name__:	List[str]							=       labels["""input_ids"""]
											return model_inputs





"""simple docstring"""


SCREAMING_SNAKE_CASE			  =  {}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""

       # if we are absent twice, or late 3 consecutive days,
       # no further prize strings are possible
       if late == 3 or absent == 2:
              return 0

       # if we have no days left, and have not failed any other rules,
       # we have a prize string
       if days == 0:
              return 1

       # No easy solution, so now we need to do the recursive calculation

       # First, check if the combination is already in the cache, and
       # if yes, return the stored value from there since we already
       # know the number of possible prize strings from this point on
       UpperCamelCase       			=		(days, absent, late)
       if key in cache:
              return cache[key]

       # now we calculate the three possible ways that can unfold from
       # this point on, depending on our attendance today

       # 1) if we are late (but not absent), the "absent" counter stays as
       # it is, but the "late" counter increases by one
       UpperCamelCase       			=		_calculate(days - 1		,   UpperCAmelCase_		,   late + 1					)

       # 2) if we are absent, the "absent" counter increases by 1, and the
       # "late" counter resets to 0
       UpperCamelCase       			=		_calculate(days - 1		,   absent + 1		,   0					)

       # 3) if we are on time, this resets the "late" counter and keeps the
       # absent counter
       UpperCamelCase       			=		_calculate(days - 1		,   UpperCAmelCase_		,   0					)

       UpperCamelCase       			=		state_late + state_absent + state_ontime

       UpperCamelCase       			=		prizestrings
       return prizestrings



def 						lowerCamelCase__      (				UpperCAmelCase_ = 30					)->				int:







       """simple docstring"""
       return _calculate(UpperCAmelCase_		,   absent=0		,   late=0					)


if __name__ == "__main__":
      print(solution())





